Battery management system, battery management method, and method of manufacturing battery assembly

ABSTRACT

A battery management system includes a control device and a storage. The storage stores at least one trained neural network. The trained neural network includes an input layer that accepts input data that represents a numeric value for each pixel in an image where a prescribed CCV waveform (a CCV charging waveform or a CCV discharging waveform) of a secondary battery is drawn in a region constituted of a predetermined number of pixels, and when input data is input to the input layer, the trained neural network outputs a full charge capacity of the secondary battery. The control device estimates the full charge capacity of a target battery by inputting input data obtained for the target battery into the input layer of the trained neural network.

This nonprovisional application is based on Japanese Patent ApplicationNo. 2019-200785 filed with the Japan Patent Office on Nov. 5, 2019, theentire contents of which are hereby incorporated by reference.

BACKGROUND Field

The present disclosure relates to a battery management system, a batterymanagement method, and a method of manufacturing the battery assembly.

Description of the Background Art

An alternating-current (AC) impedance method (which is also referred toas the “AC-IR method” below) has been known as an approach to evaluationof characteristics of a secondary battery. For example, Japanese PatentLaying-Open No. 2003-317810 discloses a method of determining whether ornot minor short-circuiting has occurred in a secondary battery based ona reaction resistance value of the secondary battery obtained by theAC-IR method.

SUMMARY

Electrically powered vehicles (for example, electric vehicles or hybridvehicles) including a secondary battery as a motive power source haveincreasingly been used in recent years. From a point of view ofefficient use of resources, reuse of the secondary battery used in theelectrically powered vehicle (which is also referred to as a “usedbattery” below) has been studied. For example, a system that estimates afull charge capacity of a used battery collected from an electricallypowered vehicle, determines reusability based on the estimated fullcharge capacity, and determines an application (a manner of reuse) maybe provided.

In general, a battery assembly to be mounted on a vehicle includes aplurality of (for example, several to more than ten) modules and each ofthe plurality of modules includes a plurality of (for example, severalten) cells. In general, a secondary battery that makes up a batteryassembly is referred to as a “cell.” With further prevalence of electricvehicles in the future, the number of used batteries is expected toabruptly increase. Based on such expectation, the inventors of thepresent application propose a method of estimating a full chargecapacity of a large number of used batteries highly accurately and morequickly in a more simplified manner.

Though a method of estimating a full charge capacity of a secondarybattery based on a result of measurement with the AC-IR method describedpreviously may be available, such a method suffers from problems pointedout below. In the AC-IR method, dedicated equipment such as a frequencyresponse analyzer and a potentiogalvanostat is used. Such dedicatedequipment is expensive. Furthermore, measurement conditions (temperaturecontrol, wiring of a measurement cable, and power supply noise) in theAC-IR method are strict and analysis for finding a circuit constant ofan equivalent circuit from a result of measurement is complicated.Measurement with the AC-IR method requires expert knowledge and is noteasy. Though the AC-IR method can allow relatively quick measurement,rapidity is not yet sufficient and there is a room for improvement alsoin an aspect of time for measurement.

The present disclosure was made to solve the problems above, and anobject thereof is to estimate a full charge capacity of a secondarybattery highly accurately and quickly in a simplified manner.

A battery management system according to the present disclosure managesinformation on a secondary battery. The battery management systemincludes a storage and an estimation device described below. The storagestores at least one trained neural network. The estimation deviceestimates, by using the trained neural network, a full charge capacityof a target battery that is a prescribed secondary battery. The trainedneural network includes an input layer that accepts input data thatrepresents a numeric value for each pixel in an image where a prescribedCCV waveform of the secondary battery is drawn in a region constitutedof a predetermined number of pixels, and when the input data is input tothe input layer, the trained neural network outputs the full chargecapacity of the secondary battery. The prescribed CCV waveform is anyone of a CCV charging waveform that represents transition of a CCVduring constant-current charging of the secondary battery and a CCVdischarging waveform that represents transition of a CCV duringconstant-current discharging of the secondary battery. The estimationdevice estimates the full charge capacity of the target battery byinputting the input data obtained for the target battery into the inputlayer of the trained neural network.

The “CCV” stands for a closed circuit voltage. The “secondary battery”is a rechargeable battery and includes an aqueous electrolyte solutionbattery, a non-aqueous electrolyte solution battery, an all-solid-statebattery, and a fuel cell. The “secondary battery” may be a cell, amodule (which may be denoted as an “MDL” below) including a plurality ofcells, or a battery assembly constituted of a plurality of cellselectrically connected to one another.

In the battery management system, the trained neural network is used forestimation of a full charge capacity. The inventors of the presentapplication have noted correlation of each of a CCV charging waveformand a CCV discharging waveform obtained during constant-current chargingand constant-current discharging with a full charge capacity. Accuracyin estimation by using the trained neural network is mainly determinedby quality and quantity of training. By appropriately training anuntrained neural network, the trained neural network that can allowhighly accurate estimation of the full charge capacity of a secondarybattery is obtained. When input data is input to such a trained neuralnetwork, the full charge capacity of the secondary battery is output.The number of man-hours for estimation is small and estimation can bemade in a simplified manner. Furthermore, a time period required forestimation is short. By using the trained neural network as above, thefull charge capacity of the secondary battery can be estimated highlyaccurately and quickly in a simplified manner without using a specialmeasurement instrument or performing complicated calculation.

The full charge capacity of the secondary battery corresponds to aquantity of electricity stored in the secondary battery when thesecondary battery is fully charged. The full charge capacity of thesecondary battery lowers as the secondary battery deteriorates. The“full charge capacity” means a full charge capacity of a secondarybattery at the current time point unless otherwise specified.Specifically, when the secondary battery has deteriorated at the currenttime point, the “full charge capacity” means the full charge capacity ofthe deteriorated secondary battery. The full charge capacity of thesecondary battery in an initial state (that is, the secondary batterythat has not deteriorated) may be denoted as an “initial capacity”below.

The battery management system may further include a charging apparatusand a first generation device which will be described below. Thecharging apparatus obtains the prescribed CCV waveform that is the CCVcharging waveform for the target battery by carrying outconstant-current charging of the target battery. The first generationdevice generates the input data from the prescribed CCV waveformobtained by the charging apparatus. The estimation device may estimatethe full charge capacity of the target battery by inputting the inputdata generated by the first generation device into the input layer ofthe trained neural network.

The battery management system can obtain a prescribed CCV waveform (thatis, a CCV charging waveform) by carrying out constant-current chargingof a target battery, generate input data for the trained neural network,and estimate the full charge capacity of the target battery by using thetrained neural network. In order to obtain the prescribed CCV waveform,the charging apparatus may carry out constant-current charging, forexample, for a period not shorter than one minute and not longer thanthirty minutes at a constant current value selected from a range from 1A to 5 A.

An SOC range of the prescribed CCV waveform used for generation of theinput data by the first generation device may be a prescribed SOC rangeselected from a range not lower than 0% and not higher than 10%.

A state of charge (SOC) represents a remaining capacity of stored powerand is expressed as a ratio of a current amount of stored power to anamount of stored power in a fully charged state, for example, within arange from 0 to 100%. A state in which a battery stores substantially noelectricity may be denoted as an “empty state” below. It has beenconfirmed in experiments conducted by the inventors of the presentapplication that the full charge capacity of the secondary battery canhighly accurately be estimated based on a CCV charging waveform duringinitial charging (more specifically, charging from the empty state to aprescribed SOC equal to or lower than 10%) in generation of input datafor a trained neural network. Since the CCV charging waveform duringinitial charging can be obtained by carrying out constant-currentcharging of an empty target battery for a short period of time, it iseasy to obtain the same. The prescribed SOC range may be a range of theSOC from 0% to 5%.

Any battery management system described above may further include adischarging apparatus and a second generation device which will bedescribed below. The discharging apparatus obtains the prescribed CCVwaveform which is the CCV discharging waveform for the target battery bycarrying out constant-current discharging of the target battery. Thesecond generation device generates the input data from the prescribedCCV waveform obtained by the discharging apparatus. The estimationdevice may estimate the full charge capacity of the target battery byinputting the input data generated by the second generation device intothe input layer of the trained neural network.

The battery management system can obtain a prescribed CCV waveform (thatis, a CCV discharging waveform) by carrying out constant-currentdischarging of the target battery, generate input data for the trainedneural network, and estimate the full charge capacity of the targetbattery by using the trained neural network.

The at least one trained neural network stored in the storage mayinclude a plurality of trained neural networks. The estimation devicemay obtain information on the target battery, select one trained neuralnetwork corresponding to the target battery from among the plurality oftrained neural networks based on the obtained information, and estimatethe full charge capacity of the target battery by using the selectedtrained neural network.

For example, a user can train each neural network in accordance withcharacteristics of each battery managed by the battery management systemand have the storage store a plurality of trained neural networksadapted to the batteries. The estimation device can highly accuratelyestimate the full charge capacity of the target battery by selecting andusing one trained neural network corresponding to the target batteryfrom among the plurality of trained neural networks. The plurality oftrained neural networks stored in the storage may be managed as beingdistinguished based on information on the battery. Examples of theinformation on the battery include a battery manufacturer, a structure(for example, a dimension, a shape, and a material) of the battery, andan initial capacity of the battery.

Any battery management system described above may further include asorting device that determines an application of the target batterybased on the full charge capacity estimated by the estimation device.

The battery management system can determine an application of eachbattery based on the full charge capacity of each battery. Each batterycan thus be managed for each application. The battery management systemis suitable, for example, for reuse of the battery.

A battery management method according to the present disclosure includesfirst to fourth steps which will be described below. In the first step,a prescribed CCV waveform of a target battery that is a prescribedsecondary battery is obtained. In the second step, input data for atrained neural network is generated from the prescribed CCV waveformobtained in the first step. In the third step, the input data generatedin the second step is input to the trained neural network. In the fourthstep, the trained neural network estimates (outputs) a full chargecapacity of the target battery. The input data is data that represents anumeric value for each pixel in an image where the prescribed CCVwaveform of the target battery is drawn in a region constituted of apredetermined number of pixels. The prescribed CCV waveform is any oneof a CCV charging waveform that represents transition of a CCV duringconstant-current charging of the target battery and a CCV dischargingwaveform that represents transition of a CCV during constant-currentdischarging of the target battery.

According to the battery management method as well, similarly to thebattery management system described previously, by using the trainedneural network, the full charge capacity of the secondary battery can beestimated highly accurately and quickly in a simplified manner withoutusing a special measurement instrument or performing complicatedcalculation.

An analysis apparatus may perform the first to fourth steps inaccordance with an instruction from a user. The first to fourth stepsmay fully be automated. For example, a computer may automaticallyperform the first to fourth steps based on a signal from a sensor. Thefirst to fourth steps may be performed by a plurality of computers or asingle computer.

The battery management method may include, before the first step,performing a preparation process for each vehicle to store a pluralityof secondary batteries in an empty state. The preparation process mayinclude steps of collecting the secondary battery from a vehicle,carrying out remaining capacity discharging of the collected secondarybattery, and storing the secondary battery in an empty state after theremaining capacity discharging. In the first step, the target batterymay be selected from among the plurality of secondary batteries storedin the empty state, and by carrying out constant-current charging of thetarget battery, the prescribed CCV waveform which is the CCV chargingwaveform may be obtained for the target battery.

“Remaining capacity discharging” means that the battery is dischargeduntil it is empty (that is, a state in which the battery storessubstantially no electricity). The secondary battery tends todeteriorate when it is left stand in a high SOC state. According to thebattery management method, deterioration of a battery during storage canbe suppressed by storing a plurality of secondary batteries collectedfrom each of a plurality of vehicles in the empty state. By carrying outconstant-current charging of a target battery from the empty state, theCCV charging waveform during initial charging described previously canreadily be obtained. According to the battery management method, thefull charge capacity of each secondary battery collected from eachvehicle can be estimated highly accurately and quickly in a simplifiedmanner.

The vehicle may be an electrically powered vehicle. The electricallypowered vehicle refers to a vehicle that travels with electric powerstored in a secondary battery. Examples of the electrically poweredvehicle include an electric vehicle (EV), a hybrid vehicle (HV), and aplug-in hybrid vehicle (PHV) as well as a fuel cell vehicle (FC vehicle)and a range extender EV.

Any battery management method described above may further include, afterthe fourth step, sorting the target battery based on the full chargecapacity estimated in the fourth step and shipping the target battery inaccordance with a result of sorting.

“Shipment” means taking out batteries from a storage location. Accordingto the battery management method, the full charge capacity of the targetbattery can be estimated highly accurately and quickly in a simplifiedmanner before shipment of the target battery. Thereafter, by sortingtarget batteries based on the estimated full charge capacity andshipping the target batteries in accordance with a result of sorting, aprocess from estimation of the full charge capacity until shipmentsmoothly proceeds. When constant-current charging of the target batteryis carried out in the first step, by continuing this charging, thetarget battery may fully be charged by the time of shipment.

A method of manufacturing a battery assembly according to the presentdisclosure includes first to fifth steps which will be described below.In the first step, a prescribed CCV waveform of a target battery that isa prescribed secondary battery is obtained. In the second step, inputdata for a trained neural network is generated from the prescribed CCVwaveform obtained in the first step. In the third step, the input datagenerated in the second step is input to the trained neural network. Inthe fourth step, the trained neural network estimates (outputs) a fullcharge capacity of the target battery. In the fifth step, a batteryassembly is manufactured from a plurality of secondary batteries ofwhich full charge capacity has been estimated by the trained neuralnetwork. The input data is data that represents a numeric value for eachpixel in an image where the prescribed CCV waveform of the targetbattery is drawn in a region constituted of a predetermined number ofpixels. The prescribed CCV waveform is any one of a CCV chargingwaveform that represents transition of a CCV during constant-currentcharging of the target battery and a CCV discharging waveform thatrepresents transition of a CCV during constant-current discharging ofthe target battery.

According to the method of manufacturing a battery assembly, a fullcharge capacity of the secondary battery can be estimated highlyaccurately and quickly in a simplified manner by using the trainedneural network and a battery assembly can be manufactured from aplurality of secondary batteries with an appropriate full chargecapacity based on a result of estimation.

The first to fourth steps are the same as the first to fourth steps inthe battery management method described previously. The fifth step maybe performed by a user himself/herself or by a manufacturing apparatusin accordance with an instruction from the user. The first to fifthsteps may fully be automated.

The foregoing and other objects, features, aspects and advantages of thepresent disclosure will become more apparent from the following detaileddescription of the present disclosure when taken in conjunction with theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram showing one manner of distribution from collectionto manufacturing and sales of battery packs in an embodiment of thepresent disclosure.

FIG. 2 is a diagram showing an exemplary communication networkconstructed in a battery management system according to the embodimentof the present disclosure.

FIG. 3 is a flowchart showing an exemplary work performed after amanagement entity collects battery packs from a vehicle.

FIG. 4 is a diagram showing overview of the battery management systemaccording to the embodiment of the present disclosure.

FIG. 5 is a diagram for illustrating training of a neural networkaccording to the embodiment of the present disclosure.

FIG. 6 is a diagram showing an exemplary training image used in trainingof the neural network shown in FIG. 5.

FIG. 7 is a diagram showing four CCV charging waveforms measured for alithium ion battery in Example of the present disclosure.

FIG. 8 is a diagram showing four CCV discharging waveforms measured fora lithium ion battery in Example of the present disclosure.

FIG. 9 is a diagram showing a result of evaluation of a first trained NNaccording to Example of the present disclosure.

FIG. 10 is a diagram showing a result of evaluation of a second trainedNN according to Example of the present disclosure.

FIG. 11 is a diagram showing exemplary NN information held by a batterymanagement apparatus shown in FIG. 4.

FIG. 12 is a diagram showing exemplary battery information held by thebattery management apparatus shown in FIG. 4.

FIG. 13 is a flowchart showing processing performed by a measurementapparatus in a battery management method according to the embodiment ofthe present disclosure.

FIG. 14 is a flowchart showing processing performed by the batterymanagement apparatus in the battery management method according to theembodiment of the present disclosure.

FIG. 15 is a diagram showing overview of estimation of a full chargecapacity by using a trained NN in the battery management methodaccording to the embodiment of the present disclosure.

FIG. 16 is a diagram showing a manner of shipment of a target batteryaccording to the embodiment of the present disclosure.

FIG. 17 is a diagram showing a modification of the processing shown inFIG. 13.

FIG. 18 is a diagram for illustrating a modification of the batterymanagement system.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

An embodiment of the present disclosure will be described in detail withreference to the drawings. The same or corresponding elements in thedrawings have the same reference characters allotted and descriptionthereof will not be repeated.

In the embodiment which will be described below, a used battery assemblycollected from a vehicle is reused. The battery assembly includes aplurality of modules. The plurality of modules that make up the batteryassembly may be connected in series or in parallel. Each of theplurality of modules includes a plurality of cells that are electricallyconnected (for example, in series). Though any secondary battery can beadopted as a cell that makes up the battery assembly, a lithium ionbattery and a nickel metal hydride battery will mainly be describedbelow as representative examples of a vehicle-mounted battery.

“Reuse” of a battery assembly is broadly categorized into reuse of allcells (which is also referred to as “full reuse” below), reuse of somecells (which is also referred to as “partial reuse” below), and resourcerecycle. Partial reuse is further categorized into use of parts andrebuild.

In full reuse, a battery assembly collected from a vehicle is reusedafter appropriate reconditioning processing and a prescribed performancetest, without replacement of a cell.

In partial reuse, a battery assembly collected from a vehicle isdisassembled into prescribed reuse units (for example, modules orcells). A plurality of reuse units are thus obtained.

In use of parts, a plurality of reuse units obtained by disassembly ofthe battery assembly are subjected to prescribed shipment inspection andthe reuse units that have passed the shipment inspection are shipped.

In rebuild, a battery assembly is manufactured from appropriate reuseunits of the plurality of reuse units obtained by disassembly of thebattery assembly. For example, a new battery assembly is manufactured bycombining a plurality of appropriate reuse units. Alternatively, a partof the battery assembly is replaced with appropriate reuse units toreconstruct the battery assembly. A battery assembly thus manufactured(that is, a rebuilt product) is shipped after it passes the shipmentinspection.

In resource recycle, a battery assembly collected from a vehicle isdisassembled into battery materials and a reclaimable material is takenout of the battery assembly.

FIG. 1 is a diagram showing one manner of distribution from collectionto manufacturing and sales of battery packs in the embodiment of thepresent disclosure. A manner of distribution shown in FIG. 1 is referredto as a “battery distribution model” below.

Referring to FIG. 1, the battery distribution model is constructed of abattery management entity 30 (which is also simply referred to as a“management entity 30” below), a battery pack manufacturer 34 (which isalso simply referred to as a “manufacturer 34” below), a dealer 35, arecycler 36, and a battery management system which will be describedbelow.

FIG. 2 is a diagram showing an exemplary communication networkconstructed in the battery management system according to thisembodiment. Referring to FIG. 2, a battery management system 1 includesa vehicle 10, a management server 20, terminals 40, 44, and 45, and acommunication network 50. Vehicle 10, management server 20, andterminals 40, 44, and 45 can communicate with one another overcommunication network 50. Communication network 50 may be a networkconstructed of the Internet and a base station. Communication network 50includes a base station 51. Vehicle 10 can transmit and receiveinformation through wireless communication to and from base station 51over communication network 50. Though FIG. 2 shows only a single vehicle10, battery management system 1 includes a plurality of vehicles (forexample, vehicles 60-1, 60-2, 60-3, . . . shown in FIG. 1). Managementserver 20 can communicate with all vehicles registered in advance.Terminals 40, 44, and 45 are terminals of management entity 30,manufacturer 34, and dealer 35 shown in FIG. 1, respectively.

Management entity 30 shown in FIG. 1 collects used battery packs fromvehicles 60-1, 60-2, 60-3, . . . shown in FIG. 1. Vehicles 60-1, 60-2,60-3, . . . incorporate battery packs 62-1, 62-2, 62-3, . . . ,respectively, and each battery pack includes a battery assemblyconstituted of a plurality of modules. Management entity 30 may receivefrom dealer 35, used battery packs collected from vehicle 10.

FIG. 3 is a flowchart showing an exemplary work performed aftermanagement entity 30 collects a battery pack from a vehicle.

Referring to FIG. 3 together with FIGS. 1 and 2, in step (which is alsosimply denoted as “S” below) 11, management entity 30 takes out abattery assembly from a battery pack collected from a vehicle,disassembles the battery assembly, and collects reuse units. The reuseunit may be a cell or a module which is a set of cells. In thisembodiment, a module (MDL) is defined as the reuse unit.

In S12, management entity 30 carries out remaining capacity dischargingof each of a plurality of reuse units (that is, a plurality of modules)obtained by disassembly of a battery assembly. Each cell in the reuseunit thus becomes empty (that is, stores substantially no electricity).

In S13, management entity 30 stores the plurality of reuse units (thatis, the plurality of modules) for which remaining capacity dischargingwas carried out, at a prescribed storage location (for example, astorage space 500 shown in FIG. 4 which will be described later).Management entity 30 provides for each module to be stored, an ID (whichis also referred to as an “M-ID” below) for identifying the module. Interminal 40, information on each module stored by management entity 30is managed as being associated with the M-ID. Management entity 30transmits the M-ID and module information to management server 20 byusing terminal 40. Though FIG. 1 shows only management entity 30, thereis a battery management entity for each location. Module informationmanaged by the battery management entity at each location is collectedfrom the battery management entity at each location to management server20. Management server 20 collectively manages module information managedby the battery management entity at each location, as beingdistinguished for each location (that is, for each battery managemententity).

S11 to S13 are performed for each vehicle. The plurality of modules arethus stored in an empty state (that is, a state in which substantiallyno electricity is stored). By storing the plurality of modules collectedfrom each of the plurality of vehicles in the empty state, deteriorationof modules during storage can be suppressed. Management entity 30 mayperform reconditioning processing on the reuse units before S12.Reconditioning processing is processing for reconditioning a restorablebattery (for example, recovery from high-rate deterioration or failureof an accessory). Reconditioning processing may be charging ordischarging for recovery from high-rate deterioration or may bereplacement of an accessory.

Referring again to FIGS. 1 and 2, management entity 30 conducts aperformance test on the stored reuse units (in this embodiment, themodules) and sorts the reuse units in accordance with a result of thetest. In this embodiment, the full charge capacity is estimated in theperformance test. By sorting the reuse units, an application of thereuse units is determined. An application may be determined in anymanner, and for example, the reuse units may be sorted into reuse unitsfor use of parts/rebuild/resource recycle or into batteries forvehicles/stationary batteries. The batteries for vehicles may further besorted into batteries for large vehicles/batteries for compact vehicles,and the stationary batteries may further be sorted into batteries forhouseholds/batteries for buildings/batteries for stores/batteries forfactories. The stationary battery may be used for regulation of supplyand demand of renewable energy (for example, solar energy).

The reuse unit (in this embodiment, the module) sorted into “resourcerecycle” by management entity 30 is passed from management entity 30 torecycler 36. Recycler 36 disassembles the reuse units received frommanagement entity 30 into battery materials to thereby reclaim thematerials for use as new cells or as source materials for otherproducts.

The reuse unit (in this embodiment, the module) sorted into “rebuild” bymanagement entity 30 is passed from management entity 30 to manufacturer34. Manufacturer 34 manufactures battery assemblies from the reuse unitsreceived from management entity 30 and mounts the manufactured batteryassembly on a battery pack. Manufacturer 34 brings the battery pack tocompletion by attaching an accessory (for example, a constrained plate,a sensor, and a relay) to the battery assembly.

The battery pack including the battery assembly manufactured bymanufacturer 34 is passed from manufacturer 34 to dealer 35. Dealer 35sells the battery pack, sells a new vehicle incorporating the batterypack, or accepts an order for rebuild (reconstruction) of the batteryassembly used in a vehicle.

Dealer 35 provides for each vehicle, an ID (which is also referred to asa “vehicle ID” below) for identifying the vehicle, and managesinformation on each vehicle as being associated with the vehicle ID interminal 45. Information on the vehicle includes a communication addressof communication equipment mounted on each vehicle. Dealer 35 transmitsthe vehicle ID and information on each vehicle to management server 20by using terminal 45. Though FIG. 1 shows only dealer 35, there is adealer at each location. Information on the vehicle managed by thedealer at each location is collected from the dealer at each locationand each vehicle to management server 20. Management server 20collectively manages information on the vehicle managed by the dealer ateach location, as being distinguished for each location (that is, foreach dealer).

In the battery distribution model shown in FIG. 1, when a user ofvehicle 10 who has felt a poor condition of the battery pack bringsvehicle 10 to dealer 35 and asks the dealer to repair the battery pack,for example, the battery pack is repaired in the following procedure.

The user of vehicle 10 passes vehicle 10 over dealer 35. Dealer 35provides a vehicle ID to vehicle 10. When the vehicle ID has alreadybeen provided to vehicle 10, the vehicle ID is not provided. Dealer 35determines whether or not the battery assembly mounted on vehicle 10 isfully reusable.

When vehicle 10 is determined as being fully reusable, dealer 35restores the battery assembly by performing reconditioning processingwithout replacing cells. The battery assembly that has undergonereconditioning processing is mounted on vehicle 10 after the performancetest.

When vehicle 10 is determined as not being fully reusable, dealer 35places an order with manufacturer 34 to rebuild the battery assembly. Atthis time, dealer 35 sends the battery pack collected from vehicle 10 tomanufacturer 34 together with the vehicle ID. Manufacturer 34 requestsmanagement entity 30 to provide reuse units (in this embodiment,modules) necessary for rebuild, and receives the reuse units frommanagement entity 30. Then, manufacturer 34 rebuilds the batteryassembly from the received reuse units. The battery pack including themanufactured battery assembly (that is, a rebuilt product) is deliveredto dealer 35 to which vehicle 10 has been brought, and mounted onvehicle 10 at dealer 35 after the performance test.

Though management entity 30, manufacturer 34, and dealer 35 areindividual entities in FIG. 1, classification of entities is not limitedas such. For example, a single entity may serve as management entity 30,manufacturer 34, and dealer 35.

The battery management system according to this embodiment is providedat a location of management entity 30. The battery management systemmanages information on a secondary battery. Though details will bedescribed later, the battery management system according to thisembodiment estimates the full charge capacity of the secondary batteryby using a trained neural network. FIG. 4 is a diagram showing overviewof the battery management system according to this embodiment.

Referring to FIG. 4 together with FIGS. 1 and 2, the battery managementsystem according to this embodiment includes an analysis apparatus thatobtains information on a secondary battery and storage space 500 wheresecondary batteries are stored. The analysis apparatus includes ameasurement apparatus 100, a battery management apparatus 200, a powersupply apparatus 510, a charger-discharger 520, and a sensor module 530.In this embodiment, measurement apparatus 100, power supply apparatus510, charger-discharger 520, and sensor module 530 are provided instorage space 500, and battery management apparatus 200 is providedoutside storage space 500. Battery management apparatus 200 is mounted,for example, on terminal 40 shown in FIG. 2. A plurality of automaticdischarging apparatuses 540 and a temperature control device 550 areprovided in storage space 500.

Power supply apparatus 510 is electrically connected to each ofcharger-discharger 520 and the plurality of automatic dischargingapparatuses 540. Power supply apparatus 510 includes a power storage.Management entity 30 sets a module M collected from a vehicle ontoautomatic discharging apparatus 540 in S12 in FIG. 3. This work may bedone by a user himself/herself or by a robot in accordance with aninstruction from battery management apparatus 200 or a prescribedprogram. When module M is set, automatic discharging apparatus 540automatically carries out remaining capacity discharging of module M(that is, discharging until module M is empty). Electric powerdischarged from each module M set in each automatic dischargingapparatus 540 is output to power supply apparatus 510 and stored in thepower storage. According to such a configuration, S12 (remainingcapacity discharging) and S13 (storage of the battery) in FIG. 3 cansimultaneously be performed (and a time period for the process can bereduced). Temperature control device 550 adjusts a temperature instorage space 500 to a set value while it measures a temperature instorage space 500. Temperature control device 550 may air-conditionstorage space 500. The temperature in storage space 500 is maintained ata prescribed temperature (for example, approximately 25° C.) bytemperature control device 550.

Power supply apparatus 510 supplies electric power to charger-discharger520. Power supply apparatus 510 is connected to a power grid (that is, apower grid provided by an electric utility), performs prescribed powerconversion on electric power supplied from the power grid, and suppliesresultant electric power to charger-discharger 520.

Management entity 30 conducts a performance test of a secondary batterywith the analysis apparatus before shipment of the secondary battery.Management entity 30 connects a module M_(D) (a target battery) to beshipped among a plurality of modules M stored in the empty state tocharger-discharger 520. This work may be done by a user himself/herselfor by a robot in accordance with an instruction from battery managementapparatus 200 or a prescribed program. Charger-discharger 520 carriesout constant-current charging onto module M_(D) with electric powersupplied from power supply apparatus 510. More specifically,charger-discharger 520 charges module M_(D) at a constant current valueby converting electric power supplied from power supply apparatus 510into electric power suitable for constant-current charging and supplyingresultant electric power to module M_(D).

Charger-discharger 520 includes a relay that switches between connectionand disconnection of a power path from power supply apparatus 510 tomodule M_(D) and a power conversion circuit (neither of which is shown).The power conversion circuit may include at least one of a rectificationcircuit, a power factor correction (PFC) circuit, an insulating circuit(for example, an insulating transformer), a DC/DC converter, aninverter, and a filter circuit. Each of the relay and the powerconversion circuit included in charger-discharger 520 is controlled by acontrol device 110 of measurement apparatus 100. Charger-discharger 520charges module M_(D) in accordance with an instruction from controldevice 110. Charger-discharger 520 discharges module M_(D) in accordancewith an instruction from control device 110 and outputs electric poweroutput from module M_(D) to power supply apparatus 510.

Sensor module 530 includes various sensors that detect a state (forexample, a voltage, a current, and a temperature) of module M_(D)connected to charger-discharger 520 and outputs a result of detection tomeasurement apparatus 100. An output from sensor module 530 is input tocontrol device 110 of measurement apparatus 100. Control device 110 canobtain the state (for example, a temperature, a current, a voltage, anSOC, and an internal resistance) of module M_(D) based on the outputfrom sensor module 530.

Measurement apparatus 100 includes control device 110, a storage 120,and a communication apparatus 130. Battery management apparatus 200includes a control device 210, a storage 220, a communication apparatus230, an input apparatus 240, and a display apparatus 250. Amicrocomputer including a processor and a random access memory (RAM) canbe adopted as each of control devices 110 and 210. A central processingunit (CPU) can be adopted as the processor. Each of storages 120 and 220includes, for example, a read only memory (ROM) and a rewritablenon-volatile memory. Each of storages 120 and 220 stores not only aprogram but also information (for example, a map, a mathematicalexpression, and various parameters) to be used by a program. Any numberof processors may be provided in measurement apparatus 100 and batterymanagement apparatus 200 and a processor may be prepared for eachprescribed type of control.

Each of communication apparatuses 130 and 230 includes a prescribedcommunication interface (I/F). Measurement apparatus 100 and batterymanagement apparatus 200 can communicate with each other throughcommunication apparatuses 130 and 230. Communication may be wired orwireless.

Input apparatus 240 accepts an input from a user. Input apparatus 240 isoperated by a user and outputs a signal corresponding to the operationby the user to control device 210. For example, the user can input aprescribed instruction or request to control device 210 or set aparameter value in control device 210 through input apparatus 240.Communication may be wired or wireless. Various pointing devices (amouse and a touch pad), a keyboard, or a touch panel can be adopted asinput apparatus 240. Input apparatus 240 may be an operation portion ofa portable device (for example, a notebook personal computer, asmartphone, or a wearable device).

Display apparatus 250 shows information input from control device 210.Control device 210 can give information to a user through displayapparatus 250. Examples of display apparatus 250 include a cathode raytube (CRT) display, a liquid crystal display (LCD), and a touch paneldisplay. Display apparatus 250 may be a display of a portable device.Display apparatus 250 may perform a speaker function.

Control device 110 includes a charging and discharging controller 111and an information manager 112. Each of these components is implemented,for example, by a processor and a program executed by the processor.Without being limited as such, each of these components may beimplemented by dedicated hardware (electronic circuitry).

Charging and discharging controller 111 controls charger-discharger 520to control charging and discharging of module M_(D) connected tocharger-discharger 520. In this embodiment, charging and dischargingcontroller 111 controls charger-discharger 520 to carry outconstant-current charging of module M_(D) and to obtain a CCV chargingwaveform of module M_(D) (that is, a waveform that represents transitionof a CCV during constant-current charging of module M_(D)). Informationmanager 112 receives a result of detection by sensor module 530. Duringconstant-current charging of module M_(D), sensor module 530successively detects a voltage (that is, a CCV) of module M_(D) andsuccessively outputs the detected CCV to information manager 112. TheCCV charging waveform is input to information manager 112. In thisembodiment, charging and discharging controller 111 controlscharger-discharger 520 to carry out constant-current charging of moduleM_(D) under a condition of a current value of 2 A. The CCV chargingwaveform during initial charging of module M_(D) is thus input toinformation manager 112. The condition for constant-current charging(for example, a current value) is not limited to the above and anycondition can be set.

Information manager 112 detects the CCV of module M_(D) in real timebased on the output from sensor module 530 and has storage 120 recordthe CCV charging waveform that represents transition of the CCV ofmodule M_(D). When information manager 112 finishes obtaining the CCVcharging waveform, it uses the CCV charging waveform recorded in storage120 to generate inspection image data, and has storage 120 store thegenerated inspection image data. The inspection image data is data thatrepresents a numeric value for each pixel in an image where the CCVcharging waveform is drawn in a region constituted of a predeterminednumber of pixels, and corresponds to input data to be provided to atrained neural network which will be described later. A graph format andan image format of the inspection image data are the same as those of atraining image (see FIG. 6) which will be described later. Charging anddischarging controller 111, information manager 112, power supplyapparatus 510, charger-discharger 520, and sensor module 530 accordingto this embodiment correspond to an exemplary “charging apparatus”according to the present disclosure. Information manager 112 accordingto this embodiment corresponds to an exemplary “first generation device”according to the present disclosure.

Control device 210 includes an information manager 211, a capacityestimator 212, and a sorter 213. Each of these components isimplemented, for example, by a processor and a program executed by theprocessor. Without being limited as such, each of these components maybe implemented by dedicated hardware (electronic circuitry).

Information manager 211 obtains inspection image data for module M_(D)from measurement apparatus 100. Control device 110 of measurementapparatus 100 generates inspection image data for module M_(D) as above,for example, in response to a request from a user and transmits thegenerated inspection image data to control device 210 throughcommunication apparatuses 130 and 230. Information manager 211 thenreceives the inspection image data transmitted from control device 110to control device 210.

Capacity estimator 212 estimates the full charge capacity of moduleM_(D) by inputting the inspection image data obtained for module M_(D)into an input layer of a trained neural network. The trained neuralnetwork is stored in storage 220. Sorter 213 determines an applicationof module M_(D) based on the full charge capacity of module M_(D)estimated by capacity estimator 212. Capacity estimator 212 and sorter213 according to this embodiment correspond to an exemplary “estimationdevice” and an exemplary “sorting device” according to the presentdisclosure, respectively.

FIG. 5 is a diagram for illustrating training of a neural network.Referring to FIG. 5, a trained neural network is obtained byappropriately training an untrained neural network. In this embodiment,management entity 30 obtains a trained neural network by supervisedmachine learning of an untrained neural network. In this embodiment, theuntrained neural network refers to a general-purpose machine learningalgorithm, and the trained neural network functions as an estimationmodel that estimates the full charge capacity of the secondary battery.

The neural network includes an input layer x, a hidden layer y, and anoutput layer z. Each of a training image and inspection image data areinput to input layer x. Input layer x includes nodes corresponding innumber (N) to the number of pixels in the training image (and theinspection image data). The number of nodes of output layer z isdetermined in accordance with the number of necessary outputs. Forexample, when output layer z includes 101 nodes, output layer z canoutput a result of estimation of the full charge capacity in incrementsof 0.1 Ah within a range from 0 Ah to 10 Ah, output a result ofestimation of the full charge capacity in increments of 0.1 Ah within arange from 15 Ah to 25 Ah, or output a result of estimation of the fullcharge capacity in increments of 0.2 Ah within a range from 0 Ah to 20Ah. Though any number of nodes of output layer z can be set, the numberof nodes of output layer z is set to 71 in this embodiment.

The inventors of the present application have noted correlation of eachof a CCV charging waveform and a CCV discharging waveform with the fullcharge capacity. The CCV charging waveform refers to a CCV waveform thatrepresents transition of a CCV during constant-current charging of asecondary battery. The CCV discharging waveform refers to a CCV waveformthat represents transition of a CCV during constant-current dischargingof a secondary battery.

In this embodiment, an untrained neural network is subjected tosupervised machine learning by using as teaching data, a CCV chargingwaveform training image and ground truth data (that is, data on anactually measured full charge capacity of the secondary battery). TheCCV charging waveform training image is a training image that expressesa CCV charging waveform of a secondary battery in prescribed graphformat and image format. The trained neural network with which the fullcharge capacity of the secondary battery can highly accurately beestimated with the image data of the CCV charging waveform (morespecifically, image data expressed in a graph format and an image formatthe same as those for the CCV charging waveform training image) beingused as input data is thus obtained. In addition, in this embodiment, anuntrained neural network is subjected to supervised machine learning byusing as teaching data, a CCV discharging waveform training image andground truth data (that is, data on an actually measured full chargecapacity of the secondary battery). The CCV discharging waveformtraining image is a training image that expresses a CCV dischargingwaveform of a secondary battery in prescribed graph format and imageformat. The trained neural network with which the full charge capacityof the secondary battery can highly accurately be estimated with theimage data of the CCV discharging waveform (more specifically, imagedata expressed in a graph format and an image format the same as thosefor the CCV discharging waveform training image) being used as inputdata is thus obtained.

FIG. 6 is a diagram showing an exemplary training image used in trainingof a neural network. The training image shown in FIG. 6 is data thatrepresents a numeric value (that is, a pixel value) for each pixel in animage where a CCV discharging waveform is drawn in a region constitutedof a predetermined number of pixels. The region where the CCV waveformis drawn is also referred to as an “image region” below. The number ofpixels in the image region is set, for example, to approximately 12000.In an exemplary image region, the number of pixels in a verticaldirection is set to approximately sixty and the number of pixels in ahorizontal direction is set to approximately two hundred. In thetraining image shown in FIG. 6, density of black and white (from whiteto black) is expressed in a 256-level (0 to 255) gray scale. A pixelvalue of 0 represents white and a pixel value of 255 represents black.Each pixel value in the image region takes any value from 0 to 255 and avalue of the pixel increases as the pixel is close to black. In thistraining image, the abscissa represents time and the ordinate representsthe CCV. The value of the pixel at a position closer to data of actualmeasurement (for example, a detection value from sensor module 530 shownin FIG. 4) is larger. CCVs identical in time on the abscissa areexpressed, for example, by two to three pixels. According to such graphformat and image format, the CCV waveform (that is, transition of theCCV) can highly accurately be expressed even though the number of pixelsis small.

The graph format and the image format of the training image aredescribed above with reference to an exemplary CCV discharging waveformtraining image shown in FIG. 6. The CCV discharging waveform trainingimage and the CCV charging waveform training image are identical to eachother in graph format and image format except for the CCV waveform drawnin the image region. Therefore, the CCV charging waveform training imageis not shown.

FIG. 7 is a diagram showing four CCV charging waveforms (that is, CCVwaveforms during constant-current charging) measured for a lithium ionbattery. The full charge capacity of the lithium ion battery is lower asdeterioration of the lithium ion battery proceeds. The inventors of thepresent application have measured CCV charging waveforms of anon-aqueous electrolyte solution lithium ion battery with an initialcapacity of 21 Ah at timing when the full charge capacity of the lithiumion battery deteriorates to a measurement point (20 Ah, 19 Ah, 18 Ah,and 17 Ah) while the lithium ion battery is used in an electricallypowered vehicle to deteriorate. In FIG. 7, lines L11, L12, L13, and L14represent CCV charging waveforms measured at time points when the fullcharge capacity of the lithium ion battery attains to 20 Ah, 19 Ah, 18Ah, and 17 Ah, respectively. As shown in FIG. 7, the CCV chargingwaveform correlates with the full charge capacity.

FIG. 8 is a diagram showing four CCV discharging waveforms (that is, CCVwaveforms during constant-current discharging) measured for a lithiumion battery. The inventors of the present application have measured CCVdischarging waveforms of a non-aqueous electrolyte solution lithium ionbattery with an initial capacity of 21 Ah at timing when the full chargecapacity of the lithium ion battery deteriorates to a measurement point(20 Ah, 19 Ah, 18 Ah, and 17 Ah) while the lithium ion battery is usedin an electrically powered vehicle to deteriorate. In FIG. 8, lines L21,L22, L23, and L24 represent CCV discharging waveforms measured at timepoints when the full charge capacity of the lithium ion battery attainsto 20 Ah, 19 Ah, 18 Ah, and 17 Ah, respectively. As shown in FIG. 8, theCCV discharging waveform correlates with the full charge capacity.

Referring again to FIG. 5, a weight W1 between input layer x and hiddenlayer y and a weight W2 between hidden layer y and output layer z areadjusted such that a target output from the neural network and an actualoutput match with each other by supervised machine learning of theneural network using the teaching data described previously. Byrepeating adjustment of weights W1 and W2 by a teaching signal, accuracyin estimation of a capacity by using the neural network can be enhanced.

The inventors of the present application evaluated, with a method whichwill be described below, accuracy in estimation of a capacity by usingeach of a first trained NN and a second trained NN. The first trained NNis a trained neural network trained to estimate a full charge capacityof a nickel metal hydride battery with an initial capacity of 7 Ah basedon image data of CCV charging waveforms. The second trained NN is atrained neural network trained to estimate a full charge capacity of anon-aqueous electrolyte solution lithium ion battery with an initialcapacity of 21 Ah based on image data of CCV discharging waveforms.

The inventors of the present application prepared 336 pieces ofevaluation data for each trained NN. For evaluation data for evaluatingthe first trained NN, an image for evaluation of the CCV chargingwaveforms and ground truth data (that is, an actually measured value ofthe full charge capacity) of the nickel metal hydride battery with theinitial capacity of 7 Ah were prepared. For evaluation data forevaluating the second trained NN, an image for evaluation of the CCVdischarging waveforms and ground truth data (that is, an actuallymeasured value of the full charge capacity) of the non-aqueouselectrolyte solution lithium ion battery with the initial capacity of 21Ah were prepared. The inventors of the present application inputted theevaluation image into the trained NN (that is, the first trained NN orthe second trained NN) and obtained an output value from the trained NN(that is, an estimated value of the full charge capacity). The inventorsof the present application calculated an absolute value of a difference(which is referred to as an “absolute error” below) between the outputvalue from the trained NN and the actually measured value of the fullcharge capacity. The inventors of the present application calculated theabsolute error for each piece of evaluation data and obtained 336absolute errors. The inventors of the present application calculated aratio (which is also referred to as an “evaluation result A” below) ofthe number of absolute errors equal to or smaller than 0.5 Ah of the 336absolute errors, and further calculated an average value (which is alsoreferred to as an “evaluation result B” below) of the 336 absoluteerrors. In evaluation, accuracy in estimation of the capacity by usingthe trained NN is evaluated from two points of view. As evaluationresult A exhibits a higher ratio and evaluation result B exhibits asmaller value, accuracy in estimation of a capacity by using the trainedNN is evaluated as being higher.

FIG. 9 is a diagram showing a result of evaluation of the first trainedNN. Referring to FIG. 9, in evaluation of the first trained NN,evaluation result A was 88.54% and evaluation result B was 0.22 Ah. Bythus using the first trained NN, the full charge capacity of thesecondary battery (more specifically, the nickel metal hydride batterywith the initial capacity of 7 Ah) could highly accurately be estimated.

FIG. 10 is a diagram showing a result of evaluation of the secondtrained NN. Referring to FIG. 10, in evaluation of the second trainedNN, evaluation result A was 92.38% and evaluation result B was 0.21 Ah.By thus using the second trained NN, the full charge capacity of thesecondary battery (more specifically, the non-aqueous electrolytesolution lithium ion battery with the initial capacity of 21 Ah) couldhighly accurately be estimated.

In this embodiment, a trained neural network (which is also referred toas a “charging type NN” below) that includes an input layer that acceptsfirst input data that represents a numeric value for each pixel in animage where a CCV charging waveform of the secondary battery is drawn ina region constituted of a predetermined number of pixels and outputs afull charge capacity of the secondary battery when the first input datais input to the input layer is used. The first trained NN describedabove corresponds to an exemplary charging type NN. By training asdescribed above, however, a trained neural network (which is alsoreferred to as a “discharging type NN” below) that includes an inputlayer that accepts second input data that represents a numeric value foreach pixel in an image where a CCV discharging waveform of a secondarybattery is drawn in a region constituted of a predetermined number ofpixels and outputs a full charge capacity of the secondary battery whenthe second input data is input to the input layer can also be generated.The second trained NN described above corresponds to an exemplarydischarging type NN. An embodiment (a modification) using thedischarging type NN will be described later.

Referring again to FIG. 4, the trained neural network that allowssufficiently high accuracy in estimation of a capacity as a result oftraining as described above is stored in storage 220. In thisembodiment, a plurality of trained neural networks are stored in storage220. The plurality of trained neural networks are managed as beingdistinguished from one another based on a battery manufacturer and amodel (see FIG. 11 which will be described later). Conditions formanufacturing of a battery are different for each battery manufacturer.The model represents a structure (for example, a dimension, a shape, anda material) and an initial capacity of a battery. A plurality ofbatteries being identical in battery manufacturer and model means thatthe structure, the initial capacity, and the conditions formanufacturing of the batteries are substantially the same. Each trainedneural network is a neural network trained by using secondary batteriescorresponding in battery manufacturer and model. Each of the pluralityof trained neural networks stored in storage 220 corresponds to anexemplary charging type NN. The neural network may be denoted as “NN”below.

Storage 220 stores information (which is also referred to as “NNinformation” below) that associates a battery manufacturer and a modelwith a trained NN. FIG. 11 is a diagram showing exemplary NNinformation. Referring to FIG. 11, NN information associates a batterymanufacturer and a model with a trained NN (NX-1, NX-2, NY-1, . . . ).The NN information shown in FIG. 11 includes information on each trainedNN (for example, a training condition). Examples of the trainingcondition include a graph format and an image format of input data, acharging rate during training, and a temperature during training.

Referring again to FIG. 4, storage 220 stores information (which is alsoreferred to as “battery information” below) on each module M stored instorage space 500 as being distinguished based on the M-ID. FIG. 12 is adiagram showing exemplary battery information. Referring to FIG. 12, thebattery information includes, for example, a battery manufacturer, amodel, a battery material (a non-aqueous electrolyte solution lithiumion battery, a nickel metal hydride battery, an all-solid-state lithiumion battery, . . . ), and an initial capacity. The battery informationmay further include information not shown in FIG. 12 (for example, adimension and a shape of a battery).

Referring again to FIG. 4, control device 210 controls display apparatus250 to show, in response to a request from a user, at least one of theNN information (see, for example, FIG. 11) and the battery information(see, for example, FIG. 12). The user can check information on eachtrained NN stored in storage 220 and information on each module M storedin storage space 500 by giving an instruction to control device 210through input apparatus 240.

When any of a plurality of modules M stored in storage space 500 is tobe shipped, the user connects that to-be-shipped module M tocharger-discharger 520. Module M connected to charger-discharger 520 ishandled as module M_(D) (the target battery). The user requests controldevice 210 to sort module M_(D) through input apparatus 240, beforeshipment of module M_(D). At this time, the user inputs the M-ID ofmodule M_(D) to control device 210. Information manager 211 of controldevice 210 requests control device 110 of measurement apparatus 100 totransmit inspection image data, in response to the request from theuser. Control device 110 controls charger-discharger 520 to carry outconstant-current charging of module M_(D) in response to the requestfrom information manager 211, generates inspection image data fromoutputs from sensor module 530, and transmits the generated inspectionimage data to information manager 211. Information manager 211 sends thereceived inspection image data to capacity estimator 212.

Capacity estimator 212 obtains the battery manufacturer and the model ofmodule M_(D) from the battery information in storage 220 and the M-IDinput by the user. Capacity estimator 212 then selects one trained NNcorresponding to the battery manufacturer and the model of module M_(D),from among the plurality of trained NNs stored in storage 220, byreferring to the NN information in storage 220. The trained NN adaptedto module M_(D) is thus selected. Thereafter, capacity estimator 212estimates the full charge capacity of module M_(D) by inputting theinspection image data for module M_(D) received from information manager211 into the input layer of the trained NN selected as above.

The full charge capacity (a result of estimation) of module M_(D)estimated by capacity estimator 212 is sent from capacity estimator 212to sorter 213. Sorter 213 determines an application of module M_(D)based on the result of estimation. Sorter 213 writes the determinedapplication into the battery information in storage 220 and sends theapplication to information manager 211. When information manager 211receives the application of module M_(D), it has display apparatus 250show the application. The user looks at a screen on display apparatus250 to know the result of sorting (that is, the application of moduleM_(D)), and ships module M_(D) in accordance with the result of sorting.

FIG. 13 is a flowchart showing processing performed by control device110 of measurement apparatus 100 in response to a request frominformation manager 211 of battery management apparatus 200. Processingshown in this flowchart is started when information manager 211 requestscontrol device 110 to transmit inspection image data in S31 in FIG. 14which will be described later.

Referring to FIG. 13 together with FIG. 4, in S21, charging anddischarging controller 111 starts constant-current charging of moduleM_(D). A condition (for example, a charging rate) in constant-currentcharging may be predetermined or designated by a user.

In S221, information manager 112 obtains an output (including a CCV ofmodule M_(D)) from sensor module 530 during constant-current chargingand has storage 120 record the output. In S222, information manager 112determines whether or not it has completed obtainment of a CCV chargingwaveform. During a period until obtainment of the CCV charging waveformis completed (that is, during a period over which determination as NO ismade in S222), S221 and S222 are repeatedly performed. In thisembodiment, when an SOC of module M_(D) exceeds a prescribed SOC value(for example, 5%), determination as YES (obtainment of the CCV chargingwaveform having been completed) is made in S222 and the process proceedsto S23. The condition for determination in S222, however, is not limitedto the above. For example, when a prescribed time period (for example,1200 seconds) has elapsed since start of constant-current charging inS21, information manager 112 may make determination as YES in S222. Inthis embodiment, S21, S221, and S222 in FIG. 13 correspond to anexemplary “first step” according to the present disclosure.

In S23, information manager 112 generates inspection image data from theCCV charging waveforms recorded in storage 120 and has storage 120 storethe generated inspection image data. In this embodiment, S23 in FIG. 13corresponds to an exemplary “second step” according to the presentdisclosure.

In S24, information manager 112 transmits the inspection image datagenerated in S23 to battery management apparatus 200. Thereafter,information manager 112 stands by in S25 until it receives a result ofsorting (that is, a result of sorting transmitted in S37 in FIG. 14which will be described later) from battery management apparatus 200.Charging started in S21 is continued also while the information managerstands by in S25 (that is, during a period over which determination asNO is made in S25). Though charging is continued without change incondition for charging after start of charging in S21 in thisembodiment, the condition for charging may be changed at the timing whendetermination as YES is made in S222. For example, control device 110may increase the charging rate.

When information manager 112 receives the result of sorting from batterymanagement apparatus 200 (YES in S25), the process proceeds to S26. Inthis embodiment, module M_(D) is sorted into any of use ofparts/rebuild/resource recycle based on the result of sorting. In S26,charging and discharging controller 111 determines whether or not moduleM_(D) has been sorted into resource recycle. When determination as NO(module M_(D) having been sorted into use of parts or rebuild) is madein S26, in S27, charging and discharging controller 111 determineswhether or not a condition for completion of charging is satisfied.During a period over which the condition for completion of charging isnot satisfied (that is, during a period over which determination as NOis made in S27), charging and discharging controller 111 continuescharging of module M_(D). In this embodiment, when the SOC of moduleM_(D) attains to an SOC (for example, an SOC corresponding to a fullycharged state) suitable for an application (more specifically, anapplication determined in S36 which will be described later),determination as YES (the condition for completion of charging havingbeen satisfied) is made in S27, and in S28, charging and dischargingcontroller 111 stops charging of module M_(D). As processing in S28 isperformed, a series of processing in FIG. 13 ends.

When determination as YES (module M_(D) having been sorted into resourcerecycle) is made in S26, charging and discharging controller 111 stopscharging of module M_(D) and controls charger-discharger 520 to carryout remaining capacity discharging of module M_(D). As processing in S29is performed, the series of processing in FIG. 13 ends.

FIG. 14 is a flowchart showing processing performed by control device210 of battery management apparatus 200 in response to a request from auser. Processing shown in this flowchart is started when the user inputsthe M-ID of module M_(D) into control device 210 and requests forsorting of module M_(D).

Referring to FIG. 14 together with FIG. 4, in S31, information manager211 transmits a signal that requests for transmission of inspectionimage data for module M_(D) to measurement apparatus 100. Thereafter,information manager 211 stands by in S32 until it receives inspectionimage data (that is, inspection image data transmitted in S24 in FIG.13) from measurement apparatus 100.

When information manager 211 receives the inspection image data formodule M_(D) from measurement apparatus 100 (YES in S32), the processproceeds to S33. In S33, capacity estimator 212 obtains the batterymanufacturer and the model of module M_(D) from the battery informationin storage 220 and the M-ID input by the user. In S34, capacityestimator 212 selects one trained NN corresponding to the batterymanufacturer and the model of module M_(D), from among the plurality oftrained NNs stored in storage 220, by referring to the NN information instorage 220. In S35, capacity estimator 212 estimates the full chargecapacity of module M_(D) by inputting the inspection image data formodule M_(D) received from measurement apparatus 100 into the inputlayer of the trained NN selected in S34.

FIG. 15 is a diagram showing overview of estimation of a full chargecapacity by using a trained NN. As shown in FIG. 15, when inspectionimage data (an input image) of module M_(D) is input to the input layerof the trained NN in S35 in FIG. 14, the full charge capacity of moduleM_(D) is output from the output layer of the trained NN. In thisembodiment, S35 in FIG. 14 corresponds to an exemplary “third step” andan exemplary “fourth step” according to the present disclosure.

Referring again to FIG. 14 together with FIG. 4, in S36, sorter 213sorts module M_(D) based on the full charge capacity thereof estimatedin S35. Module M_(D) is sorted based on an application thereof. Then,sorter 213 has storage 220 store a result of sorting (that is, anapplication of module M_(D)).

In S36, sorter 213 initially sorts module M_(D) into any of use ofparts/rebuild/resource recycle. For example, when the full chargecapacity of module M_(D) is lower than a first threshold value, sorter213 determines the application of module M_(D) as “resource recycle.”When the full charge capacity of module M_(D) is equal to or higher thanthe first threshold value and lower than a second threshold value,sorter 213 determines the application of module M_(D) as “battery forhouseholds (use of parts).” When the full charge capacity of moduleM_(D) is equal to or higher than the second threshold value and lowerthan a third threshold value, sorter 213 determines the application ofmodule M_(D) as “battery for factories (use of parts).” When the fullcharge capacity of module M_(D) is equal to or higher than the thirdthreshold value, sorter 213 determines the application of module M_(D)as “rebuild.” The first to third threshold values satisfy such relationas “the first threshold value<the second threshold value<the thirdthreshold value.” This is one manner of sorting and can be modified asappropriate. For example, a secondary battery low in full chargecapacity may be allocated to “rebuild”. Sorter 213 may sort the moduleonly into partial reuse/resource recycle based on the estimated fullcharge capacity of module M_(D).

In S37, information manager 211 transmits the application determined inS36 to measurement apparatus 100, and in S38, it has display apparatus250 show the application. Display apparatus 250 may show the full chargecapacity estimated in S35 together with the application. As theprocessing in S38 is performed, a series of processing in FIG. 14 ends.

A user checks the result of sorting (that is, the application of moduleM_(D)) shown as a result of the processing in S38 and ships module M_(D)in accordance with the result of sorting. FIG. 16 is a diagram showingan exemplary manner of shipment of module M_(D). In FIG. 16, modules M1,M2, M3, and M4 are modules sorted into “rebuild,” “battery forhouseholds (use of parts),” “battery for factories (use of parts),” and“resource recycle” in S36 in FIG. 14, respectively.

Referring to FIG. 16, tags T1 to T3 are attached to modules M1 to M3,respectively. Tags T1 to T3 may be attached at timing of shipment ofmodules M1 to M3 or timing when modules M1 to M3 are put into storagespace 500 (for example, S11 in FIG. 3). Each of tags T1 to T3 accordingto this embodiment is an IC tag that stores the M-ID, the full chargecapacity, and the application of the corresponding module. For example,a radio frequency identification (RFID) tag can be adopted as the ICtag. Battery management apparatus 200 may read and rewrite informationstored in each of tags T1 to T3 through wireless communication. Batterymanagement apparatus 200 may write the determined application into tagsT1 to T3 when the application of modules M1 to M3 is determined in S36in FIG. 14.

Modules M1 to M3 are each shipped to a destination corresponding to theapplication determined in S36 in FIG. 14. Module M1 is shipped tomanufacturer 34 shown in FIG. 1. Modules M2 and M3 are shipped to adistributor of stationary batteries. Module M4 is sent to recycler 36shown in FIG. 1. Recycler 36 disassembles module M4 into batterymaterials to thereby reclaim the materials for use as new cells or assource materials for other products.

As described above, in the battery management system according to thisembodiment, control device 210 estimates the full charge capacity ofmodule M_(D) by inputting image data (that is, inspection image data) ofCCV charging waveforms obtained for module M_(D) (target battery) intothe input layer of the trained neural network. The method of estimatinga capacity according to this embodiment is smaller in man-hours forestimation than the AC-IR method and can allow more simplifiedestimation. Furthermore, a time period required for estimation is short.According to the battery management system, the full charge capacity ofthe secondary battery can be estimated highly accurately and quickly ina simplified manner without using a special measurement instrument orperforming complicated calculation.

In the embodiment, the secondary battery is stored at a constanttemperature (for example, 25° C.), and training and evaluation of aneural network as well as a performance test using the trained neuralnetwork are carried out at a storage temperature. All managemententities, however, are not necessarily able to store secondary batteriesat a constant temperature. In order to highly accurately estimate thefull charge capacity of the secondary battery also under a conditionwhere a storage temperature is varied, control device 210 may handle atemperature at the time of obtainment of CCV waveforms of the secondarybattery as an explanatory variable when it estimates the full chargecapacity (a response variable) of the secondary battery.

The battery management system according to the embodiment estimates thefull charge capacity of the secondary battery by using a charging typeNN. Without being limited thereto, the battery management system mayestimate the full charge capacity of the secondary battery by using adischarging type NN. An embodiment (a modification) that uses adischarging type NN will be described with reference to FIGS. 3, 4, 14,and 17.

Referring to FIG. 3, in this modification, in S12, management entity 30connects module M collected from a vehicle to charger-discharger 520instead of automatic discharging apparatus 540. As module M is connectedto charger-discharger 520, processing in FIG. 17 is started. Module Mconnected to charger-discharger 520 is handled as module M_(D) (targetbattery).

FIG. 17 is a diagram showing a modification of the processing shown inFIG. 13. Referring to FIG. 17 together with FIG. 4, in S411, chargingand discharging controller 111 controls charger-discharger 520 to adjustthe SOC of module M_(D) to a start SOC (for example, an SOCcorresponding to a fully charged state) in constant-current discharging.The start SOC in constant-current discharging may have a predeterminedvalue or may be designated by a user.

After processing in S411, in S412, charging and discharging controller111 starts constant-current discharging of module M_(D). In thismodification, charging and discharging controller 111 controlscharger-discharger 520 such that constant-current discharging of moduleM_(D) is carried out under a condition of a current value of 2 A. Thecondition (for example, a current value) for constant-currentdischarging is not limited as above and can arbitrarily be set. Thecondition (for example, a discharging rate) in constant-currentdischarging may be predetermined or designated by a user.

In S421, information manager 112 obtains an output (including a CCV ofmodule M_(D)) from sensor module 530 during constant-current dischargingand has storage 120 record the output. In S422, information manager 112determines whether or not obtainment of CCV discharging waveforms hasbeen completed. During a period until obtainment of CCV dischargingwaveforms is completed (that is, during a period over whichdetermination as NO is made in S422), S421 and S422 are repeatedlyperformed. In this modification, when the SOC of module M_(D) is lowerthan a prescribed SOC value (for example, 90%), determination as YES(obtainment of CCV discharging waveforms having been completed) is madein S422 and the process proceeds to S43. The condition for determinationin S422 is not limited as above. For example, when a prescribed timeperiod (for example, 1200 seconds) has elapsed since start ofconstant-current discharging in S412, information manager 112 may makedetermination as YES in S422. Charging and discharging controller 111,information manager 112, power supply apparatus 510, charger-discharger520, and sensor module 530 (FIG. 4) according to this modificationcorrespond to an exemplary “discharging apparatus” according to thepresent disclosure.

In S43, information manager 112 generates inspection image data from CCVdischarging waveforms recorded in storage 120 and has storage 120 storethe generated inspection image data. The inspection image data accordingto this modification is data that represents a numeric value for eachpixel in an image where CCV discharging waveforms are drawn in a regionconstituted of a predetermined number of pixels, and corresponds toinput data to be provided to a trained neural network. Informationmanager 112 (FIG. 4) according to this modification corresponds to anexemplary “second generation device” according to the presentdisclosure.

In S44, information manager 112 transmits the inspection image datagenerated in S43 to battery management apparatus 200. Thereafter, inS45, remaining capacity discharging of module M_(D) is carried out. Forexample, remaining capacity discharging of module M_(D) may be carriedout by continuing discharging started in S412 by charging anddischarging controller 111. Discharging may be continued without changein condition for discharging after start of discharging in S412, or acondition for discharging may be modified at timing when determinationas YES is made in S422. Management entity 30 may carry out remainingcapacity discharging of module M_(D) by removing module M_(D) fromcharger-discharger 520 and placing the module onto automatic dischargingapparatus 540.

In this modification, a plurality of discharging type NNs are stored instorage 220 shown in FIG. 4. The plurality of discharging type NNs aremanaged as being distinguished based on the battery manufacturer and themodel. Processing in FIG. 14 from which S37 and S38 are omitted isperformed by control device 210 of battery management apparatus 200. InS35 in FIG. 14, capacity estimator 212 estimates the full chargecapacity of module M_(D) by inputting the inspection image data (thatis, inspection image data transmitted in S44 in FIG. 17) for moduleM_(D) received from measurement apparatus 100 into the input layer ofthe trained NN (more specifically, the discharging type NN) selected inS34.

Module M that has undergone estimation of the full charge capacity andremaining capacity discharging as above is stored in storage space 500(S13 in FIG. 3). Both of the charging type NN and the discharging typeNN may be stored in storage 220 and control device 210 may selectivelyuse the charging type NN and the discharging type NN depending on atarget battery.

Storage space 500 and battery management apparatus 200 are provided atthe location of management entity 30 in the embodiment. Without beinglimited thereto, storage space 500 may be provided at the location ofmanufacturer 34 and battery management apparatus 200 may be mounted onmanagement server 20 shown in FIG. 2. The battery management systemdescribed previously may be provided at the location of manufacturer 34or dealer 35. FIG. 18 is a diagram for illustrating an exemplary batterymanagement system provided at the location of manufacturer 34.

Referring to FIG. 18 together with FIG. 1, manufacturer 34 manufacturesa battery assembly 600 (that is, a rebuilt product) by combining modulesM61 to M63 (that is, a plurality of modules with a full charge capacitysuitable for rebuild) sorted into “rebuild” in S36 in FIG. 14. This stepof manufacturing battery assembly 600 corresponds to an exemplary “fifthstep” according to the present disclosure. Battery assembly 600illustrated in FIG. 18 includes a plurality of cells 601. In batteryassembly 600, a positive electrode terminal of one cell 601 and anegative electrode terminal of another adjacent cell 601 areelectrically connected to each other by a conductive bus bar 602. Cells601 are connected in series to each other. A battery pack ismanufactured by attaching an accessory to such battery assembly 600. Tothe battery pack, a tag T6 (for example, an RFID tag) that storesinformation (for example, the M-ID and the full charge capacity) on eachmodule included in battery assembly 600 together with the battery ID ofbattery assembly 600 is attached. The battery pack including batteryassembly 600 thus manufactured is shipped after a shipment inspection.Manufacturer 34 sends a module M5 sorted into “resource recycle” in S36in FIG. 14 to recycler 36. Recycler 36 reclaims module M5.

The configuration of the battery management system is not limited to theconfiguration shown in FIG. 4. For example, automatic dischargingapparatus 540 does not have to be provided. Instead ofcharger-discharger 520 that carries out both of charging anddischarging, a charging apparatus that carries out only charging or adischarging apparatus that carries out only discharging may be adopted.Battery management apparatus 200 may be mounted on a portable device(for example, a smartphone). It is not essential that measurementapparatus 100 and battery management apparatus 200 are arranged at adistance from each other. Battery management apparatus 200 may beprovided in storage space 500 together with measurement apparatus 100.Measurement apparatus 100 and battery management apparatus 200 may beintegrated. Any number of trained neural networks or a single trainedneural network may be stored in storage 220.

Though control device 110 of measurement apparatus 100 functions as the“first generation device” and the “second generation device” in theembodiment and the modification, control device 210 of batterymanagement apparatus 200 may function as the “first generation device”and the “second generation device.” Inspection image data may begenerated in battery management apparatus 200.

It is not essential that the battery management system includes asorting device (that is, the battery management system determines anapplication of a target battery). A user may determine an application ofa target battery (for example, module M_(D)) based on the full chargecapacity of the target battery estimated by battery management apparatus200.

Though an example in which a secondary battery is collected from avehicle is mentioned in the embodiment, the battery management systemmay be applied to a secondary battery collected from another mobile body(for example, a ship, an airplane, an automated guided vehicle, anagricultural implement, or a drone), a secondary battery collected froma portable device (that is, an electronic device that can be carried bya user), or a stationary secondary battery. The target battery is notlimited to a module, but it may be a cell or a battery assembly. Thebattery management system may estimate the full charge capacity of thebattery assembly at the time of collection of the battery assembly froma vehicle (for example, S11 in FIG. 3) and sort (for example, sort intofull reuse/resource recycle) that battery assembly based on theestimated full charge capacity.

Though the embodiment of the present disclosure has been described, itshould be understood that the embodiment disclosed herein isillustrative and non-restrictive in every respect. The scope of thepresent disclosure is defined by the terms of the claims and is intendedto include any modifications within the scope and meaning equivalent tothe terms of the claims.

What is claimed is:
 1. A battery management system that managesinformation on a secondary battery, the battery management systemcomprising: a storage that stores at least one trained neural network;and an estimation device that estimates, by using the trained neuralnetwork, a full charge capacity of a target battery that is a prescribedsecondary battery, the trained neural network including an input layerthat accepts input data, the trained neural network outputting the fullcharge capacity of a secondary battery when the input data is input tothe input layer, the input data representing a numeric value for eachpixel in an image where a prescribed CCV waveform of the secondarybattery is drawn in a region constituted of a predetermined number ofpixels, the prescribed CCV waveform being any one of a CCV chargingwaveform and a CCV discharging waveform, the CCV charging waveformrepresenting transition of a CCV during constant-current charging of thesecondary battery, the CCV discharging waveform representing transitionof a CCV during constant-current discharging of the secondary battery,and the estimation device estimating the full charge capacity of thetarget battery by inputting the input data obtained for the targetbattery into the input layer of the trained neural network.
 2. Thebattery management system according to claim 1, further comprising: acharging apparatus that obtains the prescribed CCV waveform that is theCCV charging waveform for the target battery by carrying outconstant-current charging of the target battery; and a first generationdevice that generates the input data from the prescribed CCV waveformobtained by the charging apparatus, wherein the estimation deviceestimates the full charge capacity of the target battery by inputtingthe input data generated by the first generation device into the inputlayer of the trained neural network.
 3. The battery management systemaccording to claim 2, wherein an SOC range of the prescribed CCVwaveform used for generation of the input data by the first generationdevice is a prescribed SOC range selected from a range not lower than 0%and not higher than 10%.
 4. The battery management system according toclaim 1, further comprising: a discharging apparatus that obtains theprescribed CCV waveform that is the CCV discharging waveform for thetarget battery by carrying out constant-current discharging of thetarget battery; and a second generation device that generates the inputdata from the prescribed CCV waveform obtained by the dischargingapparatus, wherein the estimation device estimates the full chargecapacity of the target battery by inputting the input data generated bythe second generation device into the input layer of the trained neuralnetwork.
 5. The battery management system according to claim 1, whereinthe at least one trained neural network includes a plurality of trainedneural networks, and the estimation device obtains information on thetarget battery, selects one trained neural network corresponding to thetarget battery from among the plurality of trained neural networks basedon the obtained information, and estimates the full charge capacity ofthe target battery by using the selected trained neural network.
 6. Thebattery management system according to claim 1, further comprising asorting device that determines an application of the target batterybased on the full charge capacity estimated by the estimation device. 7.A battery management method comprising: obtaining a prescribed CCVwaveform of a target battery that is a prescribed secondary battery;generating input data for a trained neural network from the prescribedCCV waveform; and inputting the input data to the trained neuralnetwork; and estimating, by the trained neural network, a full chargecapacity of the target battery, the input data representing a numericvalue for each pixel in an image where the prescribed CCV waveform ofthe target battery is drawn in a region constituted of a predeterminednumber of pixels, the prescribed CCV waveform being any one of a CCVcharging waveform and a CCV discharging waveform, the CCV chargingwaveform representing transition of a CCV during constant-currentcharging of the target battery, the CCV discharging waveformrepresenting transition of a CCV during constant-current discharging ofthe target battery.
 8. The battery management method according to claim7, further comprising, before the obtaining a prescribed CCV waveform,performing a preparation process for each vehicle to store a pluralityof secondary batteries in an empty state, the preparation processcomprising collecting the secondary battery from a vehicle, carrying outremaining capacity discharging of the collected secondary battery, andstoring the secondary battery in the empty state after the remainingcapacity discharging, wherein the obtaining a prescribed CCV waveformincludes selecting the target battery from among the plurality ofsecondary batteries stored in the empty state, and obtaining theprescribed CCV waveform that is the CCV charging waveform for the targetbattery by carrying out constant-current charging of the target battery.9. The battery management method according to claim 7, furthercomprising, after the estimating a full charge capacity: sorting thetarget battery based on the full charge capacity estimated by thetrained neural network; and shipping the target battery in accordancewith a result of the sorting.
 10. A method of manufacturing a batteryassembly, the method comprising: obtaining a prescribed CCV waveform ofa target battery that is a prescribed secondary battery; generatinginput data for a trained neural network from the prescribed CCVwaveform; inputting the input data to the trained neural network;estimating, by the trained neural network, a full charge capacity of thetarget battery; and manufacturing a battery assembly from a plurality ofsecondary batteries of which full charge capacity has been estimated bythe trained neural network, the input data representing a numeric valuefor each pixel in an image where the prescribed CCV waveform of thetarget battery is drawn in a region constituted of a predeterminednumber of pixels, and the prescribed CCV waveform being any one of a CCVcharging waveform and a CCV discharging waveform, the CCV chargingwaveform representing transition of a CCV during constant-currentcharging of the target battery, the CCV discharging waveformrepresenting transition of a CCV during constant-current discharging ofthe target battery.